Title :
Efficient Regularized Least Squares Classification
Author :
Zhang, Peng ; Peng, Jing
Author_Institution :
Tulane University, New Orleans, LA
Abstract :
Kernel-based regularized least squares (RLS) algorithms are a promising technique for classification. RLS minimizes a regularized functional directly in a reproducing kernel Hilbert space defined by a kernel. In contrast, support vector machines (SVMs) implement the structure risk minimization principle and use the kernel trick to extend it to the nonlinear case. While both have a sound mathematical foundation, RLS is strikingly simple. On the other hand, SVMs in general have a sparse representation of the solution. In this paper, we introduce a very fast version of the RLS algorithm while maintaining the achievable level of performance. The proposed new algorithm computes solutions in O(m) time and O(1) space, where m is the number of training points. We demonstrate the efficacy of our very fast RLS algorithm using a number of (both real simulated) data sets.
Keywords :
Computer errors; Equations; Hilbert space; Kernel; Least squares methods; Resonance light scattering; Risk management; Support vector machine classification; Support vector machines; Training data;
Conference_Titel :
Computer Vision and Pattern Recognition Workshop, 2004. CVPRW '04. Conference on
DOI :
10.1109/CVPR.2004.57